Experimental comparison of machine learning-based available bandwidth estimation methods over operational LTE networks
N Sato, T Oshiba, K Nogami… - … IEEE Symposium on …, 2017 - ieeexplore.ieee.org
2017 IEEE Symposium on Computers and Communications (ISCC), 2017•ieeexplore.ieee.org
We propose PathML, an available bandwidth (ie, unused capacity of an end-to-end path)
estimation method based on a data-driven paradigm that uses machine learning with a large
amount of data. An experiment over an operational LTE network was performed to compare
our method with prior work.
estimation method based on a data-driven paradigm that uses machine learning with a large
amount of data. An experiment over an operational LTE network was performed to compare
our method with prior work.
We propose PathML, an available bandwidth (i.e., unused capacity of an end-to-end path) estimation method based on a data-driven paradigm that uses machine learning with a large amount of data. An experiment over an operational LTE network was performed to compare our method with prior work.
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